Adjacent Features Research Articles

Optimization and imputation of acoustic absorption sequence prediction for transversely isotropic porous materials: A MultiScaleCNN-Transformer-PSO model approach

To address the limitations of traditional methods in real-time prediction and adaptive optimization of sound absorption coefficients for transversely isotropic porous materials. This study introduces a method combining a Multi-Scale Convolutional Neural Network (MultiScaleCNN) and a Transformer model to improve real-time prediction and optimization of sound absorption coefficients in transversely isotropic porous materials. Using a validated transfer matrix method based on the Biot model and acoustic analysis via COMSOL 6.2, a database of sound absorption curves was created. MultiScaleCNN extracts multi-scale features, which the Transformer model uses for sequence-to-sequence modeling with a self-attention mechanism, effectively managing complex dependencies. The model achieves high accuracy in full-mask prediction, closely aligning with actual values. Additionally, Particle Swarm Optimization (PSO) optimizes sound absorption coefficients in the 1000–2000 Hz range, meeting the fitness function’s criteria. For partial-mask imputation, the model uses adjacent frequency data and features to enhance accuracy. Results show that the MultiScaleCNN-Transformer-PSO model offers a robust, data-driven approach for predicting and optimizing sound absorption coefficients, advancing the field.

Graph neural network-based anomaly detection for human cyber physical systems

ABSTRACT Human Cyber Physical Systems (HCPS) encompass humans, networks, and physical devices, characterized by intricate interactions and interdependencies. The complexity of HCPS networks makes them vulnerable to network issues and cyberattacks, potentially resulting in network paralysis and significantly impacting the reliable operation of HCPS. Therefore, precise anomaly detection is imperative for HCPS. To tackle this issue, we integrate HCPS with the Software Defined Networking (SDN) infrastructure layer and propose an anomaly detection strategy based on Graph Neural Networks (GNN). Our approach utilizes Graph Sample and Aggregates (GraphSAGE) to capture adjacent feature information of nodes, revealing hidden relationships within the graph. These embeddings are then combined with Graph Attention Networks (GAT), which calculate attention coefficients between a node and its neighboring nodes, representing the importance of neighboring nodes to the central node. Finally, a fully connected layer is employed to obtain anomaly node labels. To assess the performance of our method, we conduct experiments using real datasets and compare them with other advanced anomaly detection methods in terms of accuracy, precision, recall, F1 score, and area under the ROC curve (AUC). Our method accurately detects anomalous nodes, offering a viable solution for practical applications.

Rolling bearing fault diagnosis method based on wavelet packet logarithmic energy map

Aiming at the fault diagnosis problem of rolling bearings, a diagnosis method based on wavelet packet logarithmic energy map is proposed. Firstly, a new wavelet packet node logarithmic energy formula is improved and proposed to overcome the shortcomings of cumbersome parameter determination and strong subjectivity in the traditional wavelet packet energy formula, improve the recognition of high-frequency faults and the distinction of low-frequency fault categories, so as to fully extract the initial time-frequency domain features; secondly, the Gram angle and field idea is used to realize the conversion from one-dimensional features to two-dimensional image features, so as to construct the logarithmic energy map feature based on wavelet packet, which further considers the spatial information between adjacent features, thereby optimizing the initial time-frequency domain features and improving the significance of the obtained features. On this basis, the residual network is used to improve the accuracy of fault diagnosis classification results. Finally, through the simulation verification of the standard rolling bearing data set of Case Western Reserve University, it can be seen that the fault diagnosis model constructed by the proposed method has high diagnostic accuracy and strong generalization ability.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation.

Thanks to the advantages of the friendly annotations and the satisfactory performance, weakly-supervised semantic segmentation (WSSS) approaches have been extensively studied. Recently, the single-stage WSSS (SS-WSSS) was awakened to alleviate problems of the expensive computational costs and the complicated training procedures in multistage WSSS. However, the results of such an immature model suffer from problems of background incompleteness and object incompleteness. We empirically find that they are caused by the insufficiency of the global object context and the lack of local regional contents, respectively. Under these observations, we propose an SS-WSSS model with only the image-level class label supervisions, termed weakly supervised feature coupling network (WS-FCN), which can capture the multiscale context formed from the adjacent feature grids, and encode the fine-grained spatial information from the low-level features into the high-level ones. Specifically, a flexible context aggregation (FCA) module is proposed to capture the global object context in different granular spaces. Besides, a semantically consistent feature fusion (SF2) module is proposed in a bottom-up parameter-learnable fashion to aggregate the fine-grained local contents. Based on these two modules, WS-FCN lies in a self-supervised end-to-end training fashion. Extensive experimental results on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the effectiveness and efficiency of WS-FCN, which can achieve state-of-the-art results by 65.02% and 64.22% mIoU on PASCAL VOC 2012 val set and test set, 34.12% mIoU on MS COCO 2014 val set, respectively. The code and weight have been released at:WS-FCN.

Current Status of Research on Fault Diagnosis Using Machine Learning for Gear Transmission Systems

Gear transmission system fault diagnosis is crucial for the reliability and safety of industrial machinery. The combination of mathematical signal processing methods with deep learning technology has become a research hotspot in fault diagnosis. Firstly, the development and status of gear transmission system fault diagnosis are outlined in detail. Secondly, the relevant research results on gear transmission system fault diagnosis are summarized from the perspectives of time-domain, frequency domain, and time-frequency-domain analysis. Thirdly, the relevant research progress in shallow learning and deep learning in the field of fault diagnosis is explained. Finally, future research directions for gear transmission system fault diagnosis are summarized and anticipated in terms of the sparsity of signal analysis results, separation of adjacent feature components, extraction of weak signals, identification of composite faults, multi-factor combinations in fault diagnosis, and multi-source data fusion technology.

Spectral domain strategies for hyperspectral super-resolution: Transfer learning and channel enhance network

As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with spatial domain for HSI-SR tasks, four transfer learning strategies are proposed. The superior performance of the Relevant Channel/Random Space (RCRS) strategy reveals the importance of spectral feature reconstruction in HSI-SR tasks. Meanwhile, an interesting phenomenon has been observed that even without training on real datasets, the model can already exhibit a core or even decent super-resolution capability based solely on prior knowledge of above four strategies. Secondly, a dual-branch channel network with complementary channel feature extraction (CCFE) and adjacent channel feature extraction (ACFE) module is designed for spectral feature enhancement, which demonstrate superior performance compared to state-of-the-art methods on six datasets. To conclude, the effectiveness of RCRS strategy with pseudo prior channel knowledge on seven dual-input and eight single-input networks, as well as superiority of the proposed channel-enhanced network indicate the importance of spectral properties for HSI-SR tasks.

A unified feature-motion consistency framework for robust image matching

Establishing reliable feature matches between a pair of images in various scenarios is a long-standing open problem in photogrammetry. Attention-based detector-free matching with coarse-to-fine architecture has been a typical pipeline to build matches, but the cross-attention module with global receptive field may compromise the structural local consistency by introducing irrelevant regions (outliers). Motion field can maintain structural local consistency under the assumption that matches for adjacent features should be spatially proximate. However, motion field can only estimate local displacements between consecutive images and struggle with long-range displacements estimation in large-scale variation scenarios without spatial correlation priors. Moreover, large-scale variations may also disrupt the geometric consistency with the application of mutual nearest neighbor criterion in patch-level matching, making it difficult to recover accurate matches. In this paper, we propose a unified feature-motion consistency framework for robust image matching (MOMA), to maintain structural consistency at both global and local granularity in scale-discrepancy scenarios. MOMA devises a motion consistency-guided dependency range strategy (MDR) in cross attention, aggregating highly relevant regions within the motion consensus-restricted neighborhood to favor true matchable regions. Meanwhile, a unified framework with hierarchical attention structure is established to couple local motion field with global feature correspondence. The motion field provides local consistency constraints in feature aggregation, while feature correspondence provides spatial context prior to improve motion field estimation. To alleviate geometric inconsistency caused by hard nearest neighbor criterion, we propose an adaptive neighbor search (soft) strategy to address scale discrepancy. Extensive experiments on three datasets demonstrate that our method outperforms solid baselines, with AUC improvements of 4.73/4.02/3.34 in two-view pose estimation task at thresholds of 5°/10°/20° on Megadepth test, and 5.94% increase of accuracy at threshold of 1px in homography task on HPatches datasets. Furthermore, in the downstream tasks such as 3D mapping, the 3D models reconstructed using our method on the self-collected SYSU UAV datasets exhibit significant improvement in structural completeness and detail richness, manifesting its high applicability in wide downstream tasks. The code is publicly available at https://github.com/BunnyanChou/MOMA.

Embedded U-shaped network with cross-hierarchical feature adaptation fusion for remote sensing image haze removal

ABSTRACT Recently, U-Net architecture has been extensively explored for remote sensing (RS) image haze removal, demonstrating remarkable performance. However, most existing RS image haze removal methods based on the U-Net fail to fully utilize feature information extracted in the encoding layers during decoding, resulting in unsatisfactory haze removal results. Moreover, most haze removal algorithms neglect the interaction of cross-level feature information, which is particularly important for scene recovery and context information constraints. To address these issues, this paper presents an Embedded U-shaped Network with Cross-Hierarchical Feature Adaptation Fusion Network (EUCHA). Specifically, an Embedded U-shaped Network Framework (EUNF) is proposed to fuse the features extracted from different scale encoding layers as supplementary information into the corresponding decoding layers by embedding multiple parallel U-shaped subnetworks, which alleviates feature information dilution during the decoding process and enhances the network receptive field. Furthermore, we propose a Cross-Hierarchical Feature Adaptive Fusion (CHAF) module, which effectively and adaptively fuses multiple adjacent hierarchical features extracted by channel attention and pixel attention through learnable factors to enhance the expressive ability of features. Experiments were conducted on both real and synthetic datasets and compared with the nine most advanced algorithms. The results showed that EUCHA algorithm performs better in removing artefacts in both RS images and natural images. The source code is available at https://github.com/GongHongY/EUCHA.

A mixed Mamba U-net for prostate segmentation in MR images

The diagnosis of early prostate cancer depends on the accurate segmentation of prostate regions in magnetic resonance imaging (MRI). However, this segmentation task is challenging due to the particularities of prostate MR images themselves and the limitations of existing methods. To address these issues, we propose a U-shaped encoder-decoder network MM-UNet based on Mamba and CNN for prostate segmentation in MR images. Specifically, we first proposed an adaptive feature fusion module based on channel attention guidance to achieve effective fusion between adjacent hierarchical features and suppress the interference of background noise. Secondly, we propose a global context-aware module based on Mamba, which has strong long-range modeling capabilities and linear complexity, to capture global context information in images. Finally, we propose a multi-scale anisotropic convolution module based on the principle of parallel multi-scale anisotropic convolution blocks and 3D convolution decomposition. Experimental results on two public prostate MR image segmentation datasets demonstrate that the proposed method outperforms competing models in terms of prostate segmentation performance and achieves state-of-the-art performance. In future work, we intend to enhance the model's robustness and extend its applicability to additional medical image segmentation tasks.

A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning

With the rapid growth of social media, fake news (rumors) are rampant online, seriously endangering the health of mainstream social consciousness. Fake news detection (FEND), as a machine learning solution for automatically identifying fake news on Internet, is increasingly gaining the attentions of academic community and researchers. Recently, the mainstream FEND approaches relying on deep learning primarily involves fully supervised fine-tuning paradigms based on pre-trained language models (PLMs), relying on large annotated datasets. In many real scenarios, obtaining high-quality annotated corpora are time-consuming, expertise-required, labor-intensive, and expensive, which presents challenges in obtaining a competitive automatic rumor detection system. Therefore, developing and enhancing FEND towards data-scarce scenarios is becoming increasingly essential. In this work, inspired by the superiority of semi-/self- supervised learning, we propose a novel few-shot rumor detection framework based on semi-supervised adversarial learning and self-supervised contrastive learning, named Detection Yet See Few (DetectYSF). DetectYSF synergizes contrastive self-supervised learning and adversarial semi-supervised learning to achieve accurate and efficient FEND capabilities with limited supervised data. DetectYSF uses Transformer-based PLMs (e.g., BERT, RoBERTa) as its backbone and employs a Masked LM-based pseudo prompt learning paradigm for model tuning (prompt-tuning). Specifically, during DetectYSF training, the enhancement measures for DetectYSF are as follows: (1) We design a simple but efficient self-supervised contrastive learning strategy to optimize sentence-level semantic embedding representations obtained from PLMs; (2) We construct a Generation Adversarial Network (GAN), utilizing random noises and negative fake news samples as inputs, and employing Multi-Layer Perceptrons (MLPs) and an extra independent PLM encoder to generate abundant adversarial embeddings. Then, incorporated with the adversarial embeddings, we utilize semi-supervised adversarial learning to further optimize the output embeddings of DetectYSF during its prompt-tuning procedure. From the news veracity dissemination perspective, we found that the authenticity of the news shared by these collectives tends to remain consistent, either mostly genuine or predominantly fake, a theory we refer to as “news veracity dissemination consistency”. By employing an adjacent sub-graph feature aggregation algorithm, we infuse the authenticity characteristics from neighboring news nodes of the constructed veracity dissemination network during DetectYSF inference. It integrates the external supervisory signals from “news veracity dissemination consistency” to further refine the news authenticity detection results of PLM prompt-tuning, thereby enhancing the accuracy of fake news detection. Furthermore, extensive baseline comparisons and ablated experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of DetectYSF for few-shot fake new detection under low-resource scenarios.

MRSAPose: Multi-level routing sparse attention for multi-person pose estimation

Multi-person Pose Estimation (MPPE) aims to reconstruct human poses by locating and connecting keypoints of individuals in input images. The variability of human poses and the complexity of scenes make MPPE reliant on both local details and global structures, and the absence of either can lead to the generation of deformed poses. With the emergence of Transformer, the performance of MPPE has been significantly improved. However, due to self-attention computing attention scores between each pair of positions, the current Transformer-based MPPE exhibits high quadratic complexity. To address these issues, this paper proposes a novel pose estimation model, MRSAPose. MRSAPose utilizes Multi-level Routing Sparse Attention (MRSA) to dynamically select relevant regions for attention, reducing computational complexity and mitigating the impact of irrelevant regions. Furthermore, MRSAPose constructs a Transformer-CNN Parallel Interaction Block (T-CP block) through MRSA and Recursive Residual Gated Convolution (Res-gnConv), facilitating parallel learning of global and local information. By relying on multi-level routing algorithms and high-order spatial interactions conducted by recursive processing of adjacent features, T-CP block helps MRSAPose effectively alleviates the issues of occlusion and misalignment in pose estimation. On multiple challenging keypoint datasets, MRSAPose outperforms current state-of-the-art algorithms, particularly excelling in crowded and occluded scenes.

A Digital Twin-Based Method for Assembly Deviations Analysis

Abstract This paper proposes and develops a digital twin-based method for assembly deviation analysis, which considers the impact of product shape deviations and uses assembly data in the product design phase to enhance the performance of assembly deviation analysis. First, the overall product assembly deviation analysis process based on twin information is studied. Second, a mating algorithm, which evaluates multi-source assembly information, is proposed and developed. Third, the interaction between adjacent mating feature deviation propagation and an updated iterative mechanism is examined. Finally, a digital twin assembly deviation analysis model based on the new proposed Jacobian-twin approach is established, and validation is conducted through a case study.

Fabric defect detection based on feature enhancement and complementary neighboring information

Abstract Fabric defect detection is a crucial aspect of quality control in the textile industry. Given the complexities of fabric backgrounds, the high similarity between patterned backgrounds and defects, and the variety of defect scales, we propose a fabric defect detection method based on feature enhancement and complementary neighboring information . The core of this method lies in two main components: the feature enhancement module and the neighboring information complementation strategy. The feature enhancement module includes two sub-modules: similarity feature enhancement(SFE) and edge detail feature enhancement(EDFE). The SFE aims to capture the similarities between features to strengthen the distinction between defects and complex backgrounds, thereby highlighting the correlations among defects and the differences between defects and the background. The EDFE focuses on improving the network's ability to capture the edge details of fabrics, preventing edge information from becoming blurred or lost due to deeper network layers. The neighboring information complementation strategy consists of shallow-level information complementation(SLIC) and top-down information fusion complementation(TDIFC). The SLIC integrates newly introduced shallow features with neighboring features that have a smaller semantic gap, injecting richer detail information into the network. The TDIFC adaptively guides the interaction of information between adjacent feature maps, effectively aggregating multi-scale features to ensure information complementarity between features of different scales. Additionally, to further optimize model performance, we introduced partial convolution(PConv) in the backbone of the feature extraction network. PConv reduces redundant computations and decreases the model's parameter count. Experimental results show that our proposed method achieved an mAP@50 of 82.4%, which is a 6.6% improvement over the baseline model YOLOv8s. The average inference frame rate reached 61.8 FPS, meeting the real-time detection requirements for fabric defects. Moreover, the model demonstrated good generalization capabilities, effectively adapting to detecting defects in different types and colors of fabrics.

Hadal zones of the Southwest Pacific and east Indian oceans

The hadal zone (water depths > 6000 m) are unlike the overlying shallower marine regions (bathyal and abyssal) as it does not follow a continuum from the continental shelves to abyssal plains, but rather exhibits a globally disjunct series of discrete deep-sea habitats confined within geomorphological features. From an ecological perspective, hadal communities are often endemic to individual or adjacent features and are partitioned and isolated by geomorphological structures. To examine the size, shape, depth and degree of isolation of features where hadal fauna inhabit, this study explores the broad seafloor geomorphology, and distinctly partitioned hadal areas, across the Southwest Pacific and East Indian oceans using global bathymetric datasets. This research revealed the area occupied by hadal depths to be 716,915 km2 of which 58% are accounted for by trenches, 37% in basins and troughs, and 5% fracture zones. The largest feature in terms of area > 6000 m depth is the Wharton Basin with 218,030 km2 spanning 376 discrete areas. The largest continuous hadal habitats were the Kermadec and Tonga trenches at 145,103 and 111,951 km2 respectively, whereas features such as the Java Trench comprise two hadal components partitioned by a bathymetric high. Conversely, no physical barrier exists between the New Britain and Bougainville trenches thus any literature pertaining to hadal species or habitats from these trenches can be merged. This study highlights that the hadal zone mainly comprises two main geomorphological features (trenches and basins) that differ in size, depth and complexity. Hadal basins cover vast, generally shallower areas, comparable to abyssal plains, whereas trenches, despite a lesser footprint, represent greater depth ranges and complexity. As such, sampling designs and interpretation of ecological data must differ and hadal basins likely play an increasingly important role in understanding ecological shifts from abyssal to hadal ecosystems.

인접특성에 따른 주파수 가치산정: 3.7㎓ 대역을 중심으로

인접특성에 따른 주파수 가치산정: 3.7㎓ 대역을 중심으로

An Improved Method for Detecting Crane Wheel-Rail Faults Based on YOLOv8 and the Swin Transformer.

In the realm of special equipment, significant advancements have been achieved in fault detection. Nonetheless, faults originating in the equipment manifest with diverse morphological characteristics and varying scales. Certain faults necessitate the extrapolation from global information owing to their occurrence in localized areas. Simultaneously, the intricacies of the inspection area's background easily interfere with the intelligent detection processes. Hence, a refined YOLOv8 algorithm leveraging the Swin Transformer is proposed, tailored for detecting faults in special equipment. The Swin Transformer serves as the foundational network of the YOLOv8 framework, amplifying its capability to concentrate on comprehensive features during the feature extraction, crucial for fault analysis. A multi-head self-attention mechanism regulated by a sliding window is utilized to expand the observation window's scope. Moreover, an asymptotic feature pyramid network is introduced to augment spatial feature extraction for smaller targets. Within this network architecture, adjacent low-level features are merged, while high-level features are gradually integrated into the fusion process. This prevents loss or degradation of feature information during transmission and interaction, enabling accurate localization of smaller targets. Drawing from wheel-rail faults of lifting equipment as an illustration, the proposed method is employed to diagnose an expanded fault dataset generated through transfer learning. Experimental findings substantiate that the proposed method in adeptly addressing numerous challenges encountered in the intelligent fault detection of special equipment. Moreover, it outperforms mainstream target detection models, achieving real-time detection capabilities.

VPDet: Refined region CNN for hair follicle recognition in arbitrary angle

AbstractIn response to the growing demand for hair‐loss treatments, this study introduces the vector proposal detector (VPDet), a groundbreaking solution in hair transplant robotics. VPDet, distinct from traditional approaches, addresses the complex challenges of hair follicle detection, notably the variability in hair growth orientations and the intricacies of hair clustering. The method innovatively leverages the linear nature of hair, spanning a full 360‐degree orientation spectrum. The VPDet framework, a novel two‐stage object detection system, incorporates the vector proposal network and vector align blocks. These elements are crucial in transforming conventional anchor boxes into anchor vectors, thereby generating reference vectors across various scales and angles. The vector align block, a key innovation, uniquely combines vector and adjacent feature data to align features through shared maps. The extensive experiments, conducted on the FDU_HairFollicleDataset and an extended dataset, exhibit a remarkable enhancement in model performance, with a 51.3% increase in precision and a 20.8% boost in F1 score. The results not only demonstrate VPDet's superior capability in hair follicle recognition but also its potential in posture recognition for vector‐characteristic objects. This approach represents a significant advancement in both the field of hair transplant robotics and vector‐based object detection.

PGGNet: Pyramid gradual-guidance network for RGB-D indoor scene semantic segmentation

In RGB-D (red–green–blue and depth) scene semantic segmentation, depth maps provide rich spatial information to RGB images to achieve high performance. However, properly aggregating depth information and reducing noise and information loss during feature encoding after fusion are challenging aspects in scene semantic segmentation. To overcome these problems, we propose a pyramid gradual-guidance network for RGB-D indoor scene semantic segmentation. First, the quality of depth information is improved by a modality-enhancement fusion module and RGB image fusion. Then, the representation of semantic information is improved by multiscale operations. The two resulting adjacent features are used in a feature refinement module with an attention mechanism to extract semantic information. The features from adjacent modules are successively used to form an encoding pyramid, which can substantially reduce information loss and thereby ensure information integrity. Finally, we gradually integrate features at the same scale obtained from the encoding pyramid during decoding to obtain high-quality semantic segmentation. Experimental results obtained from two commonly used indoor scene datasets demonstrate that the proposed pyramid gradual-guidance network attains the highest level of performance in semantic segmentation, as compared to other existing methods.

Using a multi-scale approach to examine the effects of field margins and landscape features on predatory carabid communities in crop fields

Carabid beetles are major predators in agro-ecosystems. The composition of their communities within crop environments governs the pest control services they provide. Field margins and landscape features are known to affect carabid community composition, yet evidence is currently lacking that can be used to support land management decisions targeted at optimising predation services at the farm scale. We used experimental margins across a farm site to test carabid communities in crop areas, margins, and adjacent habitats sampled in the summer. We used novel subterranean trapping with standard pitfall trapping, to distinguish above ground and below ground activity of adults and larvae in different farm habitats. Crop type was the major influence on carabid communities in crop areas. This was followed by landscape influences in terms of adjacent habitat and boundary features, and whilst significant, margin type explained relatively little variance in summer carabid communities in-field. Trap type revealed differential activity by species. Responses to crop type, landscape factors, and margin type also varied by species. Overall, abundances were less in association with margins than control of no margin. Particularly, abundances were lower in the spillover zone adjacent to grass margins, and in the wildflower margins themselves. Carabid larvae showed notably higher abundances in association with an absence of field margins. Measures to boost key carabid species in crop areas should be considered at a farm scale, taking into account potential barrier effects, and potential buffer effects.